Visualization: Explore in Know Your Data
Description:
A specific binarization of the MNIST images originally used in (Salakhutdinov & Murray, 2008). This dataset is frequently used to evaluate generative models of images, so labels are not provided.
Additional Documentation: Explore on Papers With Code
Homepage: http://www.dmi.usherb.ca/~larocheh/mlpython/_modules/datasets/binarized_mnist.html
Source code:
tfds.datasets.binarized_mnist.BuilderVersions:
1.0.0(default): Initial Release
Download size:
104.68 MiBDataset size:
11.68 MiBAuto-cached (documentation): Yes
Splits:
| Split | Examples |
|---|---|
'test' |
10,000 |
'train' |
50,000 |
'validation' |
10,000 |
- Feature structure:
FeaturesDict({
'image': Image(shape=(28, 28, 1), dtype=uint8),
})
- Feature documentation:
| Feature | Class | Shape | Dtype | Description |
|---|---|---|---|---|
| FeaturesDict | ||||
| image | Image | (28, 28, 1) | uint8 |
Supervised keys (See
as_superviseddoc):NoneFigure (tfds.show_examples):
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{salakhutdinov2008quantitative,
title={On the quantitative analysis of deep belief networks},
author={Salakhutdinov, Ruslan and Murray, Iain},
booktitle={Proceedings of the 25th international conference on Machine learning},
pages={872--879},
year={2008},
organization={ACM}
}